class_docstr: Knowledge distillation loops for training and evaluation. This module is independent of
  the model definition. This class inherits from the Pytorch Lightning's LightningModule:
  https://lightning.ai/docs/pytorch/stable/common/lightning_module.html
code: def _shared_step(
      self,
      batch: dict,
  ):
      student_output = run_model(self.student_model, batch)
      self.teacher_model.eval()
      with torch.no_grad():
          teacher_output = run_model(self.teacher_model, batch)
      label = batch[self.student_model.label_key]
      loss = self._compute_loss(
          student_output=student_output,
          teacher_output=teacher_output,
          label=label,
      )
      return student_output, loss
  
imports: import logging
  from typing import Callable, List, Optional, Union
  import lightning.pytorch
  import torch
  import torch.nn.functional
  import torchmetrics
  from lightning.pytorch.utilities import grad_norm
  from omegaconf import DictConfig
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from torchmetrics.aggregation import BaseAggregator
  from ..constants import FEATURES, LOGITS, WEIGHT
  from ..models.utils import run_model
  from .utils import apply_layerwise_lr_decay, apply_single_lr, apply_two_stages_lr, get_lr_scheduler, get_optimizer
label_desc: Use image node_id 9 for calling the DistillerLitModule obj's underlying member method code with example usage: obj._shared_step(batch) and returns: student_output,  loss